Citation: | ZHANG Zhe, WANG Bilin, YU Zhezhou, et al., “Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 896-907, 2023, doi: 10.23919/cje.2021.00.230 |
[1] |
L. C. Chen, G. Papandreou, I. Kokkinos, et al., “DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, no.4, pp.834–848, 2018. doi: 10.1109/TPAMI.2017.2699184
|
[2] |
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, pp.234–241, 2015.
|
[3] |
Y. C. Wei, J. S. Feng, X. D. Liang, et al., “Object region mining with adversarial erasing: A simple classification to semantic segmentation approach,” in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.1568–1576, 2017,.
|
[4] |
X. Wang, S. D. You, X. Li, et al., “Weakly-supervised semantic segmentation by iteratively mining common object features,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.1354–1362, 2018.
|
[5] |
J. Ahn and S. Kwak, “Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.4981–4990, 2018.
|
[6] |
T. Y. Zhang, G. S. Lin, J. F. Cai, et al., “Decoupled spatial neural attention for weakly supervised semantic segmentation,” IEEE Transactions on Multimedia, vol.21, no.11, pp.2930–2941, 2019. doi: 10.1109/TMM.2019.2914870
|
[7] |
Y. Zeng, Y. Z. Zhuge , H. C. Lu, et al., “Joint learning of saliency detection and weakly supervised semantic segmentation,” in Proceedings of 2019 IEEE International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp.7222–7232, 2019.
|
[8] |
Y. C. Wei, H. X. Xiao, H. H. Shi, et al., “Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.7268–7277, 2018.
|
[9] |
B. L. Zhou, A. Khosla, A. Lapedriza, et al., “Learning deep features for discriminative localization,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.2921–2929, 2016.
|
[10] |
G. Papandreou, L. C. Chen, K. P. Murphy, et al., “Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation,” in Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, Chlie, pp.1742–1750, 2015.
|
[11] |
A. Roy and S. Todorovic, “Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation,” in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.7282–7291, 2017.
|
[12] |
S. Honari, J. Yosinski, P. Vincent, et al., “Recombinator networks: Learning coarse-to-fine feature aggregation,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.5743–5752, 2016.
|
[13] |
T. Y. Lin, P. Dollár, R. Girshick, et al., “Feature pyramid networks for object detection,” in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.936–944, 2017.
|
[14] |
M. Everingham, L. Van Gool, C. K. I. Williams, et al., “The pascal visual object classes (VOC) challenge,” International Journal of Computer Vision, vol.88, no.2, pp.303–338, 2010. doi: 10.1007/s11263-009-0275-4
|
[15] |
D. Y. Meng and L. N. Sun, “Some new trends of deep learning research,” Chinese Journal of Electronics, vol.28, no.6, pp.1087–1091, 2019. doi: 10.1049/cje.2019.07.011
|
[16] |
B. J. Zou, X. Shan, C. Z. Zhu, et al., “Deep learning and its application in diabetic retinopathy screening,” Chinese Journal of Electronics, vol.29, no.6, pp.992–1000, 2020. doi: 10.1049/cje.2020.09.001
|
[17] |
J. F. Dai, K. M. He, and J. Sun, “BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,” in Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, Chlie, pp.1635–1643, 2015.
|
[18] |
D. Lin, J. F. Dai, J. Y. Jia, et al., “ScribbleSup: Scribble-supervised convolutional networks for semantic segmentation,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.3159–3167, 2016.
|
[19] |
A. Bearman, O. Russakovsky, V. Ferrari, et al., “What’s the point: Semantic segmentation with point supervision,” in Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp.549–565, 2016.
|
[20] |
A. Kolesnikov and C. H. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” in Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp.695–711, 2016.
|
[21] |
A. Chaudhry, P. K. Dokania, and P. H. S. Torr, “Discovering class-specific pixels for weakly-supervised semantic segmentation,” in Proceedings of the British Machine Vision Conference, London, UK, pp.20.1–20.13, 2017.
|
[22] |
K. P. Li, Z. Y. Wu, K. C. Peng, et al., “Tell me where to look: Guided attention inference network,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.9215–9223, 2018.
|
[23] |
Q. B. Hou, P. T. Jiang, Y. C. Wei, et al., “Self-erasing network for integral object attention,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Granada, pp.547–557, 2018.
|
[24] |
N. Liu and J. W. Han, “DHSNet: Deep hierarchical saliency network for salient object detection,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.678–686, 2016.
|
[25] |
J. Lee, E. Kim, S. Lee, et al., “FickleNet: Weakly and semi-supervised semantic image segmentation using stochastic inference,” in Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp.5267–5276, 2019.
|
[26] |
P. T. Jiang, Q. B. Hou, Y. Cao, et al., “Integral object mining via online attention accumulation,” in Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), pp.2070–2079, 2019.
|
[27] |
Y. D. Wang, J. Zhang, M. N. Kan, et al., Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation,” in Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, pp.12272–12281, 2020.
|
[28] |
Z. L. Huang, X. G. Wang, J. S. Wang, et al., “Weakly-supervised semantic segmentation network with deep seeded region growing,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.7014–7023, 2018.
|
[29] |
W. Shimoda and K. Yanai, “Self-supervised difference detection for weakly-supervised semantic segmentation,” in Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), pp.5207–5216, 2019.
|
[30] |
J. S. Fan, Z. X. Zhang, T. N. Tan, et al., “CIAN: Cross-image affinity net for weakly supervised semantic segmentation,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, NY, USA, pp.10762–10769, 2020.
|
[31] |
X. L. Zhang, Y. C. Wei, J. S. Feng, et al., “Adversarial complementary learning for weakly supervised object localization,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.1325–1334, 2018.
|
[32] |
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA, pp.1–14, 2015.
|
[33] |
Z. F. Wu, C. H. Shen, and A. van der Hengel, “Wider or deeper: Revisiting the ReSnet model for visual recognition,” Pattern Recognition, vol.90, pp.119–133, 2019. doi: 10.1016/j.patcog.2019.01.006
|
[34] |
X. L. Wang, R. Girshick, A. Gupta, et al., “Non-local neural networks,” in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.7794–7803, 2018.
|
[35] |
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected CRFS with Gaussian edge potentials,” in Proceedings of the 25th Annual Conference on Neural Information Processing Systems, Granada, Spain, pp.109–117, 2011.
|
[36] |
B. Hariharan, P. Arbeláez, L. Bourdev, et al., “Semantic contours from inverse detectors,” in Proceedings of 2011 International Conference on Computer Vision, Barcelona, Spain, pp.991–998, 2011.
|
[37] |
N. Ketkar, “Introduction to pytorch,” in Deep Learning with Python, N. Ketkar, Ed. Apress, Berkeley, CA, USA, pp.195–208, 2017.
|
[38] |
J. Deng, W. Dong, R. Socher, et al., “ImageNet: A large-scale hierarchical image database,” in Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, pp.248–255, 2009.
|
[39] |
B. F. Zhang, J. M. Xiao, Y. C. Wei, et al., “Reliability does matter: An end-to-end weakly supervised semantic segmentation approach,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, NY, USA, pp.12765–12772, 2020.
|
[40] |
K. M. He, X. Y. Zhang, S. Q. Ren, et al., “Deep residual learning for image recognition,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.770–778, 2016.
|
[41] |
X. J. Qi, Z. Z. Liu, J. P. Shi, et al., “Augmented feedback in semantic segmentation under image level supervision,” in Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp.90–105, 2016.
|
[42] |
S. Hong, D. Yeo, S. Kwak, et al., “Weakly supervised semantic segmentation using web-crawled videos,” in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.7322–7330, 2017.
|
[43] |
Y. C. Wei, X. D. Liang, Y. P. Chen, et al., “STC: A simple to complex framework for weakly-supervised semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, no.11, pp.2314–2320, 2017. doi: 10.1109/TPAMI.2016.2636150
|
[44] |
R. C. Fan, Q. B. Hou, M. M. Cheng, et al., “Associating inter-image salient instances for weakly supervised semantic segmentation,” in Proceedings of the 15th European Conference on Computer Vision, Munich, Germany, pp.371–388, 2018.
|